Sunday, August 28, 2016

"Cycles of Insanity and Creativity Within Contemplative Neural Systems"

After more than 40 years of observing inventive artificial neural
systems at work (i.e., Creativity Machines), I have found that they are
susceptible to the very same cognitive pathologies as the brain. In
fact, as such forms of synthetic psychology are pushed toward higher
levels of creativity, the more likely they are to exhibit the classic
psychopathologies, such as schizophrenia, manic-depression, and various
attention disorders.

Finally getting up the nerve to solo publish within a culture well
outside physics and computer science, I took my chances with Elsevier’s
journal Medical Hypotheses, and was pleasantly surprised. After all,
neurobiology obeys physics. So, if the biologists consider this the
proverbial “spherical chicken in vacuum” then they should reconsider its
power in providing a streamlined, bottom-up perspective on both the
mechanics of seminal cognition and what can go wrong with that process.
True, insanity and creativity have been known to positively correlate
with one another for centuries, but I am offering the nuts-and-bolts
model, using amazingly simple mathematical principles.

The AI researcher Dr. Stephen Thaler has given an interview recently in
which he claims that his AI research will lead to sentient, cognizant
"creativity machines" within 5 years.

The research continues to accelerate.

Consciousness appears to be more like an intensive rather than extensive
property/behavior of the brain. It’s sort of like the gas law equation,
PV= nRT, with P and T being intensive and n and V extensive. So,
consciousness is intensive, but we as humans deny simpler forms of
consciousness, while fearing the scaled up version attainable via
machine intelligence.

An Imagination Engine is a trained artificial neural network that is
stimulated to generate new ideas and plans of action through a very
amazing effect that is an outgrowth of scientific experiments conducted
in 1975 by our founder, Dr. Stephen Thaler. In these initial
experiments, neural networks were trained upon a collection of patterns
representing some conceptual space (i.e., examples of either music,
literature, or known chemical compounds), and then the networks were
internally 'tickled' by randomly varying the connection weights joining
neurons. Astonishingly...MORE

For the naysayers the practical results were good enough to land some Department of Defense and NIST funding.
Creative crazies, sounds about right.